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<a href="gpu_2matrix__assign_8hpp.html">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a id="l00001" name="l00001"></a><span class="lineno">    1</span><span class="comment">/*!</span></div>
<div class="line"><a id="l00002" name="l00002"></a><span class="lineno">    2</span><span class="comment"> * \brief       Kernels for matrix-expression assignments on the gpu</span></div>
<div class="line"><a id="l00003" name="l00003"></a><span class="lineno">    3</span><span class="comment"> * </span></div>
<div class="line"><a id="l00004" name="l00004"></a><span class="lineno">    4</span><span class="comment"> * \author      O. Krause</span></div>
<div class="line"><a id="l00005" name="l00005"></a><span class="lineno">    5</span><span class="comment"> * \date        2016</span></div>
<div class="line"><a id="l00006" name="l00006"></a><span class="lineno">    6</span><span class="comment"> *</span></div>
<div class="line"><a id="l00007" name="l00007"></a><span class="lineno">    7</span><span class="comment"> *</span></div>
<div class="line"><a id="l00008" name="l00008"></a><span class="lineno">    8</span><span class="comment"> * \par Copyright 1995-2015 Shark Development Team</span></div>
<div class="line"><a id="l00009" name="l00009"></a><span class="lineno">    9</span><span class="comment"> * </span></div>
<div class="line"><a id="l00010" name="l00010"></a><span class="lineno">   10</span><span class="comment"> * &lt;BR&gt;&lt;HR&gt;</span></div>
<div class="line"><a id="l00011" name="l00011"></a><span class="lineno">   11</span><span class="comment"> * This file is part of Shark.</span></div>
<div class="line"><a id="l00012" name="l00012"></a><span class="lineno">   12</span><span class="comment"> * &lt;http://image.diku.dk/shark/&gt;</span></div>
<div class="line"><a id="l00013" name="l00013"></a><span class="lineno">   13</span><span class="comment"> * </span></div>
<div class="line"><a id="l00014" name="l00014"></a><span class="lineno">   14</span><span class="comment"> * Shark is free software: you can redistribute it and/or modify</span></div>
<div class="line"><a id="l00015" name="l00015"></a><span class="lineno">   15</span><span class="comment"> * it under the terms of the GNU Lesser General Public License as published </span></div>
<div class="line"><a id="l00016" name="l00016"></a><span class="lineno">   16</span><span class="comment"> * by the Free Software Foundation, either version 3 of the License, or</span></div>
<div class="line"><a id="l00017" name="l00017"></a><span class="lineno">   17</span><span class="comment"> * (at your option) any later version.</span></div>
<div class="line"><a id="l00018" name="l00018"></a><span class="lineno">   18</span><span class="comment"> * </span></div>
<div class="line"><a id="l00019" name="l00019"></a><span class="lineno">   19</span><span class="comment"> * Shark is distributed in the hope that it will be useful,</span></div>
<div class="line"><a id="l00020" name="l00020"></a><span class="lineno">   20</span><span class="comment"> * but WITHOUT ANY WARRANTY; without even the implied warranty of</span></div>
<div class="line"><a id="l00021" name="l00021"></a><span class="lineno">   21</span><span class="comment"> * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the</span></div>
<div class="line"><a id="l00022" name="l00022"></a><span class="lineno">   22</span><span class="comment"> * GNU Lesser General Public License for more details.</span></div>
<div class="line"><a id="l00023" name="l00023"></a><span class="lineno">   23</span><span class="comment"> * </span></div>
<div class="line"><a id="l00024" name="l00024"></a><span class="lineno">   24</span><span class="comment"> * You should have received a copy of the GNU Lesser General Public License</span></div>
<div class="line"><a id="l00025" name="l00025"></a><span class="lineno">   25</span><span class="comment"> * along with Shark.  If not, see &lt;http://www.gnu.org/licenses/&gt;.</span></div>
<div class="line"><a id="l00026" name="l00026"></a><span class="lineno">   26</span><span class="comment"> *</span></div>
<div class="line"><a id="l00027" name="l00027"></a><span class="lineno">   27</span><span class="comment"> */</span></div>
<div class="line"><a id="l00028" name="l00028"></a><span class="lineno">   28</span><span class="preprocessor">#ifndef REMORA_KERNELS_CLBLAS_MATRIX_ASSIGN_HPP</span></div>
<div class="line"><a id="l00029" name="l00029"></a><span class="lineno">   29</span><span class="preprocessor">#define REMORA_KERNELS_CLBLAS_MATRIX_ASSIGN_HPP</span></div>
<div class="line"><a id="l00030" name="l00030"></a><span class="lineno">   30</span> </div>
<div class="line"><a id="l00031" name="l00031"></a><span class="lineno">   31</span><span class="preprocessor">#include &quot;../../expression_types.hpp&quot;</span></div>
<div class="line"><a id="l00032" name="l00032"></a><span class="lineno">   32</span><span class="preprocessor">#include &quot;../../detail/traits.hpp&quot;</span></div>
<div class="line"><a id="l00033" name="l00033"></a><span class="lineno">   33</span> </div>
<div class="line"><a id="l00034" name="l00034"></a><span class="lineno">   34</span><span class="preprocessor">#include &lt;iostream&gt;</span></div>
<div class="line"><a id="l00035" name="l00035"></a><span class="lineno">   35</span><span class="keyword">namespace </span>remora{<span class="keyword">namespace </span>bindings{</div>
<div class="line"><a id="l00036" name="l00036"></a><span class="lineno">   36</span>    </div>
<div class="line"><a id="l00037" name="l00037"></a><span class="lineno">   37</span>    <span class="comment"></span></div>
<div class="line"><a id="l00038" name="l00038"></a><span class="lineno">   38</span><span class="comment">//////////////////////////////////////////////////////</span></div>
<div class="line"><a id="l00039" name="l00039"></a><span class="lineno">   39</span><span class="comment">////Apply function elementwise to Matrix</span></div>
<div class="line"><a id="l00040" name="l00040"></a><span class="lineno">   40</span><span class="comment">/////////////////////////////////////////////////////</span></div>
<div class="line"><a id="l00041" name="l00041"></a><span class="lineno">   41</span><span class="comment"></span> </div>
<div class="line"><a id="l00042" name="l00042"></a><span class="lineno">   42</span><span class="comment">// Explicitly iterating row major</span></div>
<div class="line"><a id="l00043" name="l00043"></a><span class="lineno">   43</span><span class="keyword">template</span>&lt;<span class="keyword">class</span> F, <span class="keyword">class</span> M, <span class="keyword">class</span> Orientation&gt;</div>
<div class="line"><a id="l00044" name="l00044"></a><span class="lineno">   44</span><span class="keywordtype">void</span> matrix_apply(</div>
<div class="line"><a id="l00045" name="l00045"></a><span class="lineno">   45</span>    matrix_expression&lt;M, gpu_tag&gt;&amp; m_unreg, </div>
<div class="line"><a id="l00046" name="l00046"></a><span class="lineno">   46</span>    F <span class="keyword">const</span>&amp; f_unreg,</div>
<div class="line"><a id="l00047" name="l00047"></a><span class="lineno">   47</span>    Orientation</div>
<div class="line"><a id="l00048" name="l00048"></a><span class="lineno">   48</span>){</div>
<div class="line"><a id="l00049" name="l00049"></a><span class="lineno">   49</span>    gpu::detail::meta_kernel k(<span class="stringliteral">&quot;blas_matrix_apply_dense&quot;</span>);</div>
<div class="line"><a id="l00050" name="l00050"></a><span class="lineno">   50</span>    </div>
<div class="line"><a id="l00051" name="l00051"></a><span class="lineno">   51</span>    <span class="keyword">auto</span> m = k.register_args(to_functor(m_unreg));</div>
<div class="line"><a id="l00052" name="l00052"></a><span class="lineno">   52</span>    <span class="keyword">auto</span> f = k.register_args(f_unreg);</div>
<div class="line"><a id="l00053" name="l00053"></a><span class="lineno">   53</span>    </div>
<div class="line"><a id="l00054" name="l00054"></a><span class="lineno">   54</span>    <span class="comment">//create source</span></div>
<div class="line"><a id="l00055" name="l00055"></a><span class="lineno">   55</span>    k&lt;&lt;m(k.get_global_id(0),k.get_global_id(1))&lt;&lt;<span class="stringliteral">&quot; = &quot;</span> &lt;&lt; f(m(k.get_global_id(0),k.get_global_id(1)))&lt;&lt;<span class="stringliteral">&quot;;&quot;</span>;</div>
<div class="line"><a id="l00056" name="l00056"></a><span class="lineno">   56</span>    boost::compute::kernel kernel = k.compile(m_unreg().queue().get_context());</div>
<div class="line"><a id="l00057" name="l00057"></a><span class="lineno">   57</span>    <span class="comment">//enqueue kernel</span></div>
<div class="line"><a id="l00058" name="l00058"></a><span class="lineno">   58</span>    std::size_t global_work_size[2] = {m_unreg().size1(), m_unreg().size2()};</div>
<div class="line"><a id="l00059" name="l00059"></a><span class="lineno">   59</span>    m_unreg().queue().enqueue_nd_range_kernel(kernel, 2,<span class="keyword">nullptr</span>, global_work_size, <span class="keyword">nullptr</span>);</div>
<div class="line"><a id="l00060" name="l00060"></a><span class="lineno">   60</span>}</div>
<div class="line"><a id="l00061" name="l00061"></a><span class="lineno">   61</span>    <span class="comment"></span></div>
<div class="line"><a id="l00062" name="l00062"></a><span class="lineno">   62</span><span class="comment">//////////////////////////////////////////////////////</span></div>
<div class="line"><a id="l00063" name="l00063"></a><span class="lineno">   63</span><span class="comment">////Scalar Assignment to Matrix</span></div>
<div class="line"><a id="l00064" name="l00064"></a><span class="lineno">   64</span><span class="comment">/////////////////////////////////////////////////////</span></div>
<div class="line"><a id="l00065" name="l00065"></a><span class="lineno">   65</span><span class="comment"></span> </div>
<div class="line"><a id="l00066" name="l00066"></a><span class="lineno">   66</span><span class="comment">// Explicitly iterating row major</span></div>
<div class="line"><a id="l00067" name="l00067"></a><span class="lineno">   67</span><span class="keyword">template</span>&lt;<span class="keyword">class</span> F, <span class="keyword">class</span> M, <span class="keyword">class</span> Orientation&gt;</div>
<div class="line"><a id="l00068" name="l00068"></a><span class="lineno">   68</span><span class="keywordtype">void</span> matrix_assign(</div>
<div class="line"><a id="l00069" name="l00069"></a><span class="lineno">   69</span>    matrix_expression&lt;M, gpu_tag&gt;&amp; m, </div>
<div class="line"><a id="l00070" name="l00070"></a><span class="lineno">   70</span>    <span class="keyword">typename</span> M::value_type t, </div>
<div class="line"><a id="l00071" name="l00071"></a><span class="lineno">   71</span>    Orientation o</div>
<div class="line"><a id="l00072" name="l00072"></a><span class="lineno">   72</span>){</div>
<div class="line"><a id="l00073" name="l00073"></a><span class="lineno">   73</span>    <span class="keyword">static_assert</span>(std::is_base_of&lt;dense_tag, typename M::storage_type::storage_tag&gt;::value, <span class="stringliteral">&quot;target must have dense storage for assignment&quot;</span>);</div>
<div class="line"><a id="l00074" name="l00074"></a><span class="lineno">   74</span>    <span class="keyword">auto</span> f = device_traits&lt;gpu_tag&gt;::make_bind_second(F(), t);</div>
<div class="line"><a id="l00075" name="l00075"></a><span class="lineno">   75</span>    matrix_apply(m,f,o);</div>
<div class="line"><a id="l00076" name="l00076"></a><span class="lineno">   76</span>}</div>
<div class="line"><a id="l00077" name="l00077"></a><span class="lineno">   77</span> </div>
<div class="line"><a id="l00078" name="l00078"></a><span class="lineno">   78</span><span class="comment"></span> </div>
<div class="line"><a id="l00079" name="l00079"></a><span class="lineno">   79</span><span class="comment">///////////////////////////////////////////////////////////////////////////////////////////</span></div>
<div class="line"><a id="l00080" name="l00080"></a><span class="lineno">   80</span><span class="comment">//////Matrix Assignment With Functor implementing +=,-=...</span></div>
<div class="line"><a id="l00081" name="l00081"></a><span class="lineno">   81</span><span class="comment">///////////////////////////////////////////////////////////////////////////////////////////</span></div>
<div class="line"><a id="l00082" name="l00082"></a><span class="lineno">   82</span><span class="comment"></span> </div>
<div class="line"><a id="l00083" name="l00083"></a><span class="lineno">   83</span><span class="keyword">template</span>&lt;<span class="keyword">class</span> F, <span class="keyword">class</span> M, <span class="keyword">class</span> E&gt;</div>
<div class="line"><a id="l00084" name="l00084"></a><span class="lineno">   84</span><span class="keywordtype">void</span> matrix_assign_functor(</div>
<div class="line"><a id="l00085" name="l00085"></a><span class="lineno">   85</span>    matrix_expression&lt;M, gpu_tag&gt;&amp; m_unreg, </div>
<div class="line"><a id="l00086" name="l00086"></a><span class="lineno">   86</span>    matrix_expression&lt;E, gpu_tag&gt; <span class="keyword">const</span>&amp; e_unreg,</div>
<div class="line"><a id="l00087" name="l00087"></a><span class="lineno">   87</span>    F f_unreg,</div>
<div class="line"><a id="l00088" name="l00088"></a><span class="lineno">   88</span>    row_major, row_major ,dense_tag, dense_tag</div>
<div class="line"><a id="l00089" name="l00089"></a><span class="lineno">   89</span>){</div>
<div class="line"><a id="l00090" name="l00090"></a><span class="lineno">   90</span>    <span class="comment">//create source</span></div>
<div class="line"><a id="l00091" name="l00091"></a><span class="lineno">   91</span>    gpu::detail::meta_kernel k(<span class="stringliteral">&quot;blas_matrix_assign&quot;</span>);</div>
<div class="line"><a id="l00092" name="l00092"></a><span class="lineno">   92</span>    <span class="keyword">auto</span> m = k.register_args(to_functor(m_unreg));</div>
<div class="line"><a id="l00093" name="l00093"></a><span class="lineno">   93</span>    <span class="keyword">auto</span> e = k.register_args(to_functor(e_unreg));</div>
<div class="line"><a id="l00094" name="l00094"></a><span class="lineno">   94</span>    <span class="keyword">auto</span> f = k.register_args(f_unreg);</div>
<div class="line"><a id="l00095" name="l00095"></a><span class="lineno">   95</span> </div>
<div class="line"><a id="l00096" name="l00096"></a><span class="lineno">   96</span>    <span class="keyword">auto</span> id0 = k.expr&lt;cl_uint&gt;(<span class="stringliteral">&quot;get_global_id(0)&quot;</span>);</div>
<div class="line"><a id="l00097" name="l00097"></a><span class="lineno">   97</span>    <span class="keyword">auto</span> id1 = k.expr&lt;cl_uint&gt;(<span class="stringliteral">&quot;get_global_id(1)&quot;</span>);</div>
<div class="line"><a id="l00098" name="l00098"></a><span class="lineno">   98</span>    k&lt;&lt; m(id0,id1) &lt;&lt; <span class="stringliteral">&quot;=&quot;</span> &lt;&lt; f(m(id0,id1),e(id0,id1))&lt;&lt;<span class="stringliteral">&quot;;\n&quot;</span>;</div>
<div class="line"><a id="l00099" name="l00099"></a><span class="lineno">   99</span>    <span class="comment">//enqueue kernel</span></div>
<div class="line"><a id="l00100" name="l00100"></a><span class="lineno">  100</span>    boost::compute::kernel kernel = k.compile(m_unreg().queue().get_context());</div>
<div class="line"><a id="l00101" name="l00101"></a><span class="lineno">  101</span>    std::size_t global_work_size[2] = {m_unreg().size1(), m_unreg().size2()};</div>
<div class="line"><a id="l00102" name="l00102"></a><span class="lineno">  102</span>    m_unreg().queue().enqueue_nd_range_kernel(kernel, 2,<span class="keyword">nullptr</span>, global_work_size, <span class="keyword">nullptr</span>);</div>
<div class="line"><a id="l00103" name="l00103"></a><span class="lineno">  103</span>}</div>
<div class="line"><a id="l00104" name="l00104"></a><span class="lineno">  104</span> </div>
<div class="line"><a id="l00105" name="l00105"></a><span class="lineno">  105</span><span class="comment">//dense-dense case row-major, column-major</span></div>
<div class="line"><a id="l00106" name="l00106"></a><span class="lineno">  106</span><span class="keyword">template</span>&lt;<span class="keyword">class</span> F,<span class="keyword">class</span> M, <span class="keyword">class</span> E&gt;</div>
<div class="line"><a id="l00107" name="l00107"></a><span class="lineno">  107</span><span class="keywordtype">void</span> matrix_assign_functor(</div>
<div class="line"><a id="l00108" name="l00108"></a><span class="lineno">  108</span>    matrix_expression&lt;M, gpu_tag&gt;&amp; m_unreg, </div>
<div class="line"><a id="l00109" name="l00109"></a><span class="lineno">  109</span>    matrix_expression&lt;E, gpu_tag&gt; <span class="keyword">const</span>&amp; e_unreg,</div>
<div class="line"><a id="l00110" name="l00110"></a><span class="lineno">  110</span>    F f_unreg,</div>
<div class="line"><a id="l00111" name="l00111"></a><span class="lineno">  111</span>    row_major, column_major ,dense_tag, dense_tag</div>
<div class="line"><a id="l00112" name="l00112"></a><span class="lineno">  112</span>) {</div>
<div class="line"><a id="l00113" name="l00113"></a><span class="lineno">  113</span>    <span class="keyword">typedef</span> <span class="keyword">typename</span> M::value_type value_type;</div>
<div class="line"><a id="l00114" name="l00114"></a><span class="lineno">  114</span>    std::size_t TILE_DIM = 32;</div>
<div class="line"><a id="l00115" name="l00115"></a><span class="lineno">  115</span>    <span class="keywordtype">char</span> <span class="keyword">const</span>* options =<span class="stringliteral">&quot;-DTILE_DIM=32ul&quot;</span>;</div>
<div class="line"><a id="l00116" name="l00116"></a><span class="lineno">  116</span>    <span class="comment">//There are usually not enough parallel worker threads in a local group</span></div>
<div class="line"><a id="l00117" name="l00117"></a><span class="lineno">  117</span>    <span class="comment">//to fill a tile. Therefore every parallel threads reads several elements.</span></div>
<div class="line"><a id="l00118" name="l00118"></a><span class="lineno">  118</span>    <span class="comment">//BLOCK_COLS are the number of parallel threads reading a column</span></div>
<div class="line"><a id="l00119" name="l00119"></a><span class="lineno">  119</span>    <span class="comment">//and must be a divisor of TILE_DIM</span></div>
<div class="line"><a id="l00120" name="l00120"></a><span class="lineno">  120</span>    std::size_t BLOCK_COLS = 8; </div>
<div class="line"><a id="l00121" name="l00121"></a><span class="lineno">  121</span>    </div>
<div class="line"><a id="l00122" name="l00122"></a><span class="lineno">  122</span>    <span class="comment">//create source</span></div>
<div class="line"><a id="l00123" name="l00123"></a><span class="lineno">  123</span>    gpu::detail::meta_kernel k(<span class="stringliteral">&quot;blas_matrix_assign_row_col&quot;</span>);</div>
<div class="line"><a id="l00124" name="l00124"></a><span class="lineno">  124</span>    <span class="keyword">auto</span> m = k.register_args(to_functor(m_unreg));</div>
<div class="line"><a id="l00125" name="l00125"></a><span class="lineno">  125</span>    <span class="keyword">auto</span> e = k.register_args(to_functor(e_unreg));</div>
<div class="line"><a id="l00126" name="l00126"></a><span class="lineno">  126</span>    <span class="keyword">auto</span> f = k.register_args(f_unreg);</div>
<div class="line"><a id="l00127" name="l00127"></a><span class="lineno">  127</span>    <span class="comment">//create local memory. we first copy a tile in local</span></div>
<div class="line"><a id="l00128" name="l00128"></a><span class="lineno">  128</span>    <span class="comment">// memory which gets the orientation right. Then we copy the tile</span></div>
<div class="line"><a id="l00129" name="l00129"></a><span class="lineno">  129</span>    <span class="comment">//to the target</span></div>
<div class="line"><a id="l00130" name="l00130"></a><span class="lineno">  130</span>    <span class="comment">// TILE_DIM+1 is here to avoid bank conflicts in local memory</span></div>
<div class="line"><a id="l00131" name="l00131"></a><span class="lineno">  131</span>    std::size_t size1_index = k.add_arg&lt;std::size_t&gt;(<span class="stringliteral">&quot;size1&quot;</span>);</div>
<div class="line"><a id="l00132" name="l00132"></a><span class="lineno">  132</span>    std::size_t size2_index = k.add_arg&lt;std::size_t&gt;(<span class="stringliteral">&quot;size2&quot;</span>);</div>
<div class="line"><a id="l00133" name="l00133"></a><span class="lineno">  133</span>    k &lt;&lt; <span class="stringliteral">&quot;__local &quot;</span> &lt;&lt;k.decl&lt;value_type&gt;(<span class="stringliteral">&quot;tile&quot;</span>)&lt;&lt; <span class="stringliteral">&quot;[TILE_DIM][TILE_DIM+2];\n&quot;</span>;</div>
<div class="line"><a id="l00134" name="l00134"></a><span class="lineno">  134</span>    k &lt;&lt; <span class="stringliteral">&quot;uint base_row = get_group_id(0) * TILE_DIM;\n&quot;</span>;</div>
<div class="line"><a id="l00135" name="l00135"></a><span class="lineno">  135</span>    k &lt;&lt; <span class="stringliteral">&quot;uint base_col = get_group_id(1) * TILE_DIM;\n&quot;</span>;</div>
<div class="line"><a id="l00136" name="l00136"></a><span class="lineno">  136</span>    <span class="comment">//copy indices, into local memory, note the change of direction</span></div>
<div class="line"><a id="l00137" name="l00137"></a><span class="lineno">  137</span>    <span class="comment">//also note that if we are on the boundary, the tile</span></div>
<div class="line"><a id="l00138" name="l00138"></a><span class="lineno">  138</span>    <span class="comment">// might be largerthan the amount of values to read</span></div>
<div class="line"><a id="l00139" name="l00139"></a><span class="lineno">  139</span>    k &lt;&lt; <span class="stringliteral">&quot;uint maxDim1 = min(size1-base_row,TILE_DIM);\n&quot;</span>;</div>
<div class="line"><a id="l00140" name="l00140"></a><span class="lineno">  140</span>    k &lt;&lt; <span class="stringliteral">&quot;uint maxDim2 = min(size2-base_col,TILE_DIM);\n&quot;</span>;</div>
<div class="line"><a id="l00141" name="l00141"></a><span class="lineno">  141</span>    k &lt;&lt; <span class="stringliteral">&quot;for(uint i = get_local_id(1) ; i &lt; maxDim2 &amp;&amp; get_local_id(0) &lt; maxDim1; i += get_local_size(1)){\n&quot;</span>;</div>
<div class="line"><a id="l00142" name="l00142"></a><span class="lineno">  142</span>    <span class="keyword">auto</span> row_exp = k.expr&lt;cl_uint&gt;(<span class="stringliteral">&quot;(base_row+get_local_id(0))&quot;</span>);</div>
<div class="line"><a id="l00143" name="l00143"></a><span class="lineno">  143</span>    <span class="keyword">auto</span> col_exp = k.expr&lt;cl_uint&gt;(<span class="stringliteral">&quot;(base_col+i)&quot;</span>);</div>
<div class="line"><a id="l00144" name="l00144"></a><span class="lineno">  144</span>    k &lt;&lt; <span class="stringliteral">&quot;    tile[get_local_id(0)][i] =&quot;</span> &lt;&lt; e(row_exp, col_exp)&lt;&lt;<span class="stringliteral">&quot;;\n&quot;</span>;</div>
<div class="line"><a id="l00145" name="l00145"></a><span class="lineno">  145</span>    k &lt;&lt; <span class="stringliteral">&quot;}\n&quot;</span>;</div>
<div class="line"><a id="l00146" name="l00146"></a><span class="lineno">  146</span>    k &lt;&lt; <span class="stringliteral">&quot;barrier(CLK_LOCAL_MEM_FENCE);\n&quot;</span>;<span class="comment">//wait until all threads are done with copying</span></div>
<div class="line"><a id="l00147" name="l00147"></a><span class="lineno">  147</span>    <span class="comment">// write output from local memory, again be sure that </span></div>
<div class="line"><a id="l00148" name="l00148"></a><span class="lineno">  148</span>    <span class="comment">// we do not write outside the feasible area</span></div>
<div class="line"><a id="l00149" name="l00149"></a><span class="lineno">  149</span>    k &lt;&lt; <span class="stringliteral">&quot;for(uint i = get_local_id(1); i &lt; maxDim1 &amp;&amp; get_local_id(0) &lt; maxDim2; i += get_local_size(1)){\n&quot;</span>; </div>
<div class="line"><a id="l00150" name="l00150"></a><span class="lineno">  150</span>    <span class="keyword">auto</span> target = m(k.expr&lt;cl_uint&gt;(<span class="stringliteral">&quot;(base_row + i)&quot;</span>), k.expr&lt;cl_uint&gt;(<span class="stringliteral">&quot;(base_col + get_local_id(0))&quot;</span>));</div>
<div class="line"><a id="l00151" name="l00151"></a><span class="lineno">  151</span>    k &lt;&lt; target &lt;&lt; <span class="stringliteral">&quot; = &quot;</span> &lt;&lt;f(target, k.expr&lt;cl_uint&gt;(<span class="stringliteral">&quot;tile[i][get_local_id(0)]&quot;</span>))&lt;&lt;<span class="stringliteral">&quot;;\n&quot;</span>;</div>
<div class="line"><a id="l00152" name="l00152"></a><span class="lineno">  152</span>    k &lt;&lt; <span class="stringliteral">&quot;}\n&quot;</span>;</div>
<div class="line"><a id="l00153" name="l00153"></a><span class="lineno">  153</span>    </div>
<div class="line"><a id="l00154" name="l00154"></a><span class="lineno">  154</span>    <span class="comment">//compile kernel</span></div>
<div class="line"><a id="l00155" name="l00155"></a><span class="lineno">  155</span>    </div>
<div class="line"><a id="l00156" name="l00156"></a><span class="lineno">  156</span>    boost::compute::kernel kernel = k.compile(m_unreg().queue().get_context(), options);</div>
<div class="line"><a id="l00157" name="l00157"></a><span class="lineno">  157</span>    </div>
<div class="line"><a id="l00158" name="l00158"></a><span class="lineno">  158</span>    <span class="comment">//enqueue kernel</span></div>
<div class="line"><a id="l00159" name="l00159"></a><span class="lineno">  159</span>    kernel.set_arg(size1_index, m_unreg().size1());</div>
<div class="line"><a id="l00160" name="l00160"></a><span class="lineno">  160</span>    kernel.set_arg(size2_index, m_unreg().size2());</div>
<div class="line"><a id="l00161" name="l00161"></a><span class="lineno">  161</span>    std::size_t global_work_size[2] = {(m_unreg().size1()+TILE_DIM-1) / TILE_DIM * TILE_DIM, (m_unreg().size2()+TILE_DIM-1) / TILE_DIM * BLOCK_COLS };</div>
<div class="line"><a id="l00162" name="l00162"></a><span class="lineno">  162</span>    std::size_t local_work_size[2] = {TILE_DIM, BLOCK_COLS};</div>
<div class="line"><a id="l00163" name="l00163"></a><span class="lineno">  163</span>    m_unreg().queue().enqueue_nd_range_kernel(kernel, 2,<span class="keyword">nullptr</span>, global_work_size, local_work_size);</div>
<div class="line"><a id="l00164" name="l00164"></a><span class="lineno">  164</span>}</div>
<div class="line"><a id="l00165" name="l00165"></a><span class="lineno">  165</span><span class="comment"></span> </div>
<div class="line"><a id="l00166" name="l00166"></a><span class="lineno">  166</span><span class="comment">/////////////////////////////////////////////////////////////////</span></div>
<div class="line"><a id="l00167" name="l00167"></a><span class="lineno">  167</span><span class="comment">//////Matrix Assignment implementing op=</span></div>
<div class="line"><a id="l00168" name="l00168"></a><span class="lineno">  168</span><span class="comment">////////////////////////////////////////////////////////////////</span></div>
<div class="line"><a id="l00169" name="l00169"></a><span class="lineno">  169</span><span class="comment"></span> </div>
<div class="line"><a id="l00170" name="l00170"></a><span class="lineno">  170</span><span class="keyword">template</span>&lt;<span class="keyword">class</span> M, <span class="keyword">class</span> E&gt;</div>
<div class="line"><a id="l00171" name="l00171"></a><span class="lineno">  171</span><span class="keywordtype">void</span> matrix_assign(</div>
<div class="line"><a id="l00172" name="l00172"></a><span class="lineno">  172</span>    matrix_expression&lt;M, gpu_tag&gt; &amp;m, </div>
<div class="line"><a id="l00173" name="l00173"></a><span class="lineno">  173</span>    matrix_expression&lt;E, gpu_tag&gt; <span class="keyword">const</span>&amp; e,</div>
<div class="line"><a id="l00174" name="l00174"></a><span class="lineno">  174</span>    row_major o, row_major,dense_tag t, dense_tag</div>
<div class="line"><a id="l00175" name="l00175"></a><span class="lineno">  175</span>) {</div>
<div class="line"><a id="l00176" name="l00176"></a><span class="lineno">  176</span>    matrix_assign_functor(m, e, device_traits&lt;gpu_tag&gt;::right_arg&lt;typename E::value_type&gt;(), o, o, t, t);</div>
<div class="line"><a id="l00177" name="l00177"></a><span class="lineno">  177</span>}</div>
<div class="line"><a id="l00178" name="l00178"></a><span class="lineno">  178</span> </div>
<div class="line"><a id="l00179" name="l00179"></a><span class="lineno">  179</span><span class="comment">//dense-dense case</span></div>
<div class="line"><a id="l00180" name="l00180"></a><span class="lineno">  180</span><span class="keyword">template</span>&lt;<span class="keyword">class</span> M, <span class="keyword">class</span> E&gt;</div>
<div class="line"><a id="l00181" name="l00181"></a><span class="lineno">  181</span><span class="keywordtype">void</span> matrix_assign(</div>
<div class="line"><a id="l00182" name="l00182"></a><span class="lineno">  182</span>    matrix_expression&lt;M, gpu_tag&gt; &amp;m, </div>
<div class="line"><a id="l00183" name="l00183"></a><span class="lineno">  183</span>    matrix_expression&lt;E, gpu_tag&gt; <span class="keyword">const</span>&amp; e,</div>
<div class="line"><a id="l00184" name="l00184"></a><span class="lineno">  184</span>    row_major o1, column_major o2,dense_tag t, dense_tag</div>
<div class="line"><a id="l00185" name="l00185"></a><span class="lineno">  185</span>) {</div>
<div class="line"><a id="l00186" name="l00186"></a><span class="lineno">  186</span>    matrix_assign_functor(m, e, device_traits&lt;gpu_tag&gt;::right_arg&lt;typename E::value_type&gt;(), o1, o2, t, t);</div>
<div class="line"><a id="l00187" name="l00187"></a><span class="lineno">  187</span>}</div>
<div class="line"><a id="l00188" name="l00188"></a><span class="lineno">  188</span> </div>
<div class="line"><a id="l00189" name="l00189"></a><span class="lineno">  189</span> </div>
<div class="line"><a id="l00190" name="l00190"></a><span class="lineno">  190</span>}}</div>
<div class="line"><a id="l00191" name="l00191"></a><span class="lineno">  191</span> </div>
<div class="line"><a id="l00192" name="l00192"></a><span class="lineno">  192</span><span class="preprocessor">#endif</span></div>
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